{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example showing the plot_lift_curve method \n",
    "\n",
    "In this example, we'll be plotting a lift curve to describe the classification performance of a `LogisticRegression` classifier using the breast cncer dataset from scikit-learn. Here, we'll be using the `scikitplot.metrics.plot_lift_curve` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\"\"\"\n",
    "An example showing the plot_lift_curve method used\n",
    "by a scikit-learn classifier\n",
    "\"\"\"\n",
    "from __future__ import absolute_import\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.datasets import load_breast_cancer as load_data\n",
    "# Import scikit-plot\n",
    "import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe2ce2e9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = load_data(return_X_y=True)\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X, y)\n",
    "probas = lr.predict_proba(X)\n",
    "skplt.metrics.plot_lift_curve(y_true=y, y_probas=probas)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
